Data Scientists

Who they are, what they do and why you want to be one

Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

They’re part mathematician, part computer scientist and part trend-spotter. And, because they straddle both the business and IT worlds, they’re highly sought-after and well-paid. Who wouldn’t want to be one?

They’re also a sign of the times. Data scientists weren’t on many radars a decade ago, but their sudden popularity reflects how businesses now think about big data. That unwieldy mass of unstructured information can no longer be ignored and forgotten. It’s a virtual gold mine that helps boost revenue – as long as there’s someone who digs in and unearths business insights that no one thought to look for before. Enter the data scientist.

Where did they come from?

Many data scientists began their careers as statisticians or data analysts. But as big data (and big data storage and processing technologies such as Hadoop) began to grow and evolve, those roles evolved as well. Data is no longer just an afterthought for IT to handle. It’s key information that requires analysis, creative curiosity and a knack for translating high-tech ideas into new ways to turn a profit.

The data scientist role also has academic origins. A few years ago, universities began to recognize that employers wanted people who were programmers and team players. Professors tweaked their classes to accommodate this – and some programs, such as the Institute for Advanced Analytics at North Carolina State University, prepared to churn out the next generation of data scientists. There are now more than 60 similar programs in universities around the country.

“My days can be very similar but week-to-week work can vary greatly. For a few weeks I might be working on a text mining project, and after that I could be creating a predictive model around the customer. Mixed in are meetings with others about analytics and how it can help different parts of the business.”

Data preparation: the process of converting raw data into another format so it can be more easily consumed.

Text analytics: the process of examining unstructured data to glean key business insights.

“On a typical day, I brainstorm and problem solve how to answer questions that come from the business with my team, I review analysis and recommendations completed by my staff, and I attend a variety of meetings.”

How can you become a data scientist?

Positioning yourself for a career in data science could be a smart move. You’ll have plenty of job opportunities, plus it’s a chance to work in the technology field with room for experimentation and creativity. So what’s your strategy?

If you’re a student
Choosing a university that offers a data science degree – or at least one offering classes in data science and analytics – is an important first step. Oklahoma State University, University of Alabama, Kennesaw State University, Southern Methodist University, North Carolina State University and Texas A&M are all examples of schools with data science programs.

If you’re a professional who wants to shift careers
While most data scientists have backgrounds as data analysts or statisticians, others come from non-technical fields such as business or economics. How can professionals from such diverse backgrounds end up in the same field? It’s important to look at what they have in common: a knack for solving problems, the ability to communicate well and an insatiable curiosity about how things work.

Aside from those qualities, you’ll also need a solid understanding of:

Statistics and machine learning.

Coding languages such as SAS, R or Python.

Databases such as MySQL and Postgres.

Data visualization and reporting technologies.

Hadoop and MapReduce.

If you don’t want to learn these skills on your own, take an online course or enroll in a bootcamp. And then, of course, you should network. Connect with other data scientists in your company, or find an online community. They’ll give you insider information into what data scientists do – and where you’ll find the best jobs.

When is a business ready to hire a data scientist?

Before you accept a data scientist position, there are a few things about the organization you should evaluate:

Does it deal with large amounts of data and have complex issues that need to be solved? Organizations that truly need data scientists have two things in common: They manage massive amounts of data, and they face weighty issues on a day-to-day basis. They’re typically in industries such as finance, government and pharma.

Does it value data? A company's culture has an impact on whether it should hire a data scientist. Does it have an environment that supports analytics? Does it have executive buy-in? If not, investing in a data scientist would be money down the drain.

Is it ready to change? As a data scientist, you expect to be taken seriously, and part of that entails seeing your work come to fruition. You devote your time to finding ways your business can better function. In response, a business needs to be ready – and willing – to follow through with the results of your findings.

Hiring a data scientist to guide business decisions based on data can be a leap of faith for some organizations. Make sure the business you might be working for has the right mindset – and is ready to make some changes.

“I work for an agile company, which requires me to be flexible and adapt to circumstances. Last week, for example, I was doing several tasks, including improving recommendation scores; tuning the integration with the operational content management system; creating new transformed variables based on consumer behavior to be used for affinity models; and doing some refactoring of existing performance reports/analytical dashboards."

Manuel-David Garcia
Data scientist for a midsize company in Heidelberg, GermanyRead the story